Erica Tavazzi – Page 3 – Systems Biology and Bioinformatics Group

Recently we had the pleasure to start collaborating with Precision ALS, a research programme for Amyotrophic Lateral Sclerosis (ALS) research across Europe, which brings together ALS clinicians, data scientists, and industries to provide new insights into the understanding of this rare disease.

This month, we attended a Precision ALS meeting in Basel, where clinical, scientific, and industry experts gathered to discuss the latest frontiers in the development and utilisation of predictive models for ALS. Together with our partners from the University of Torino, we presented our research work in the field and the new models and tools developed within the H2020 BRAINTEASER Project.

Building upon our collective knowledge of AI applied to ALS research, we aspire to bring our expertise and insights into the collaborative initiatives of Precision ALS, advancing the understanding and treatment of this multifaceted, complex disease.

In the context of the H2020 BRAINTEASER project, our group is in charge of developing predictive models for the progression of Amyotrophic Lateral Sclerosis (ALS) and Multiple Sclerosis (MS). In order to identify the most promising approaches to be implemented, we coordinated a systematic review of the artificial intelligence (AI) methodological landscape in ALS, focusing on patient stratification and disease progression prediction, which we performed together with the other project partners.

Out of 1604 reports, we identified 15 studies on patient stratification, 28 on ALS progression prediction, and 6 on both. We highlighted a general agreement in terms of input variable selection for both stratification and prediction of ALS progression, and in terms of prediction targets. A striking lack of validated models emerged, as well as a general difficulty in reproducing many published studies, mainly due to the absence of the corresponding parameter lists. While deep learning seems promising for prediction applications, its superiority with respect to traditional methods has not been established; there is, instead, ample room for its application in the subfield of patient stratification. Finally, an open question remains on the role of new environmental and behavioural variables collected via novel, real-time sensors.

The full article is available here.

These findings laid the groundwork for the development of our models within the project, providing valuable insights into the most effective AI methodologies for patient stratification and disease progression prediction in ALS. They are also guiding our direction in identifying key areas for further development and refinement.

Author: Erica Tavazzi

Advancing ALS Research: Collaborative Efforts with Precision ALS

Guazzo A, Longato E, Fadini GP, Morieri ML, Sparacino G, Di Camillo B. Deep-learning-based natural-language-processing models to identify cardiovascular disease hospitalisations of patients with diabetes from routine visits’ text. Sci Rep 2023.

Charting the Course: Insights from AI Methodological Review for ALS Progression in the BRAINTEASER Project

Gottardelli B, Nucciarelli L, Tavazzi E, Damiani A. GEN-RWD Sandbox: A Modular Architecture for Privacy-preserving Data Sharing for AI-driven Medical Research. In: Proc. 18th International Conference on Computational Intelligence methods for Bioinformatics and Biostatistics (CIBB) 2023.

Trescato I, Tavazzi E, Vettoretti M, Vasta R, Chiò A, Di Camillo B. Identifying extreme profiles in amyotrophic lateral sclerosis patients at diagnosis through archetypal analysis. In: Proc. VIII Congress of the National Group of Bioengineering (GNB 2023).

Longato E, Tavazzi E, Chiò A, Sparacino G, Di Camillo B. Dynamic Bayesian Networks and Transfer Learning Enable the Development of Deep Sequence-Based Models on Small-Sample Data. In: Proc. VIII Congress of the National Group of Bioengineering (GNB 2023).

Tavazzi E, Albertini D, Vettoretti M. Development of predictive models for short-term prediction of disability progression in multiple sclerosis. In: Proc. VIII Congress of the National Group of Bioengineering (GNB 2023).